▁constant ▁to ▁allow ▁direct ▁comparison . ▁Popular ▁choices ▁for ▁normal isation ▁constants ▁are ▁the ▁goal ▁target ▁value ▁of ▁the ▁corresponding ▁objective ▁( h ence ▁turning ▁all ▁devi ations ▁into ▁percent ages ) ▁or ▁the ▁range ▁of ▁the ▁corresponding ▁objective ▁( between ▁the ▁best ▁and ▁the ▁worst ▁possible ▁values , ▁hence ▁mapping ▁all ▁devi ations ▁onto ▁a ▁zero - one ▁range ). ▁For ▁decision ▁mak ers ▁more ▁interested ▁in ▁obtain ing ▁a ▁balance ▁between ▁the ▁compet ing ▁object ives , ▁Che b ys he v ▁goal ▁programming ▁is ▁used . ▁Int rodu ced ▁by ▁F lav ell ▁in ▁ 1 9 7 6 , ▁this ▁variant ▁see ks ▁to ▁minim ise ▁the ▁maximum ▁unw anted ▁deviation , ▁rather ▁than ▁the ▁sum ▁of ▁devi ations . ▁This ▁util ises ▁the ▁Che b ys he v ▁distance ▁metric . ▁ ▁Str ength s ▁and ▁weak ness es ▁ ▁A ▁major ▁strength ▁of ▁goal ▁programming ▁is ▁its ▁simplicity ▁and ▁ease ▁of ▁use . ▁This ▁accounts ▁for ▁the ▁large ▁number ▁of ▁goal ▁programming ▁applications ▁in ▁many ▁and ▁diverse ▁fields . ▁Linear ▁goal ▁program mes ▁can ▁be ▁solved ▁using ▁linear ▁programming ▁software ▁as ▁either ▁a ▁single ▁linear ▁programme , ▁or ▁in ▁the ▁case ▁of ▁the ▁lex ic ographic ▁variant , ▁a ▁series ▁of ▁connected ▁linear ▁program mes . ▁▁▁ ▁Go al ▁programming ▁can ▁hence ▁handle ▁relatively ▁large ▁numbers ▁of ▁variables , ▁constraints ▁and ▁object ives . ▁A ▁deb ated ▁weak ness ▁is ▁the ▁ability ▁of ▁goal ▁programming ▁to ▁produce ▁solutions ▁that ▁are ▁not ▁P are to ▁efficient . ▁This